1. Field of the Invention
The present invention relates to analog integrated circuit design. In particular, the present invention relates to techniques for adaptively adjusting circuit parameters to improve signal quality in an analog circuit.
2. Discussion of the Related Art
In many analog integrated circuits (e.g., electronic pre-distort or post-distort circuits, interference-cancellers, or crest-factor reducers), an adaptive system improves signal quality. In such an adaptive system, an on-chip adaptation circuit reduces a signal degradation measure (or “figure of merit”), which is sometimes referred to as the “cost” or “cost function.” In such a scheme, when the cost is reduced to a target value, preferably zero, the signal achieves its highest signal quality.
In many adaptive systems, a useful “cost” that may be used includes (a) a mean or average square of an error signal, the error signal being the difference between the actual signal achieved and the desired signal, or (b) an energy of an interfering or a desired signal within or without a specific frequency band. The cost may be expressed, for example, as a function of one or more coefficients that can be adaptively updated (“adapting coefficients”). In that case, the set of adapting coefficients can be represented by the vector c and the cost may be represented by the function ƒ(c). The goal in such an adaptive system is to minimize the function ƒ(c) over the possible values of vector c: i.e., to find the value of vector c that results in
      min    c    ⁢            f      ⁡              (        c        )              .  
Examples of algorithms that can be used to search for
      min    c    ⁢      f    ⁡          (      c      )      includes “least mean square” (LMS) and “recursive least square” (RLS). Existing algorithms are often inadequate for a given operating environment because:                Cost measurement is stochastic: in the presence of noise, measuring cost at a fixed coefficient c can result in widely fluctuating values of ƒ(c)        Cost surface is non-convex: a single, global minimum may not exist. In this case, even if the adaptation reaches a reasonably good solution, a better solution may exist elsewhere.        Cost surface has “long, narrow valleys”: (mathematically speaking, the matrix that characterizes the quadratic term of the cost surface has a large eigen-spread). In this case, a conventional gradient-based algorithm (e.g., LMS) takes far too many iterations to converge to an acceptable solution, even when a stochastic or non-convex cost surface is not present.        High computational complexity: in practice, the search algorithm is required to operate in real time in a low-power integrated circuit.        
Existing algorithms may be able to perform under some of the conditions described above. For example, RLS has reasonable performance under the “long, narrow valleys” condition. RLS, however, does not perform well under the stochastic cost condition, and is not an algorithm of low computational complexity. Similarly, simulated annealing is a method designed for non-convex function conditions, but does not perform well under a stochastic cost.